drop python 3.7 support (#889)

whisper/transcribe.py

@@ -26,6 +26,7 @@ def transcribe(
     logprob_threshold: Optional[float] = -1.0,
     no_speech_threshold: Optional[float] = 0.6,
     condition_on_previous_text: bool = True,
+    initial_prompt: Optional[str] = None,
     **decode_options,
 ):
     """
@@ -138,10 +139,11 @@ def transcribe(
     all_segments = []
     prompt_reset_since = 0
 
-    initial_prompt = decode_options.pop("initial_prompt", None) or []
-    if initial_prompt:
-        initial_prompt = tokenizer.encode(" " + initial_prompt.strip())
-        all_tokens.extend(initial_prompt)
+    if initial_prompt is not None:
+        initial_prompt_tokens = tokenizer.encode(" " + initial_prompt.strip())
+        all_tokens.extend(initial_prompt_tokens)
+    else:
+        initial_prompt_tokens = []
 
     def add_segment(
         *, start: float, end: float, text_tokens: torch.Tensor, result: DecodingResult
@@ -243,7 +245,11 @@ def transcribe(
             pbar.update(min(num_frames, seek) - previous_seek_value)
             previous_seek_value = seek
 
-    return dict(text=tokenizer.decode(all_tokens[len(initial_prompt):]), segments=all_segments, language=language)
+    return dict(
+        text=tokenizer.decode(all_tokens[len(initial_prompt_tokens):]),
+        segments=all_segments,
+        language=language
+    )
 
 
 def cli():
@@ -292,21 +298,18 @@ def cli():
         args["language"] = "en"
 
     temperature = args.pop("temperature")
-    temperature_increment_on_fallback = args.pop("temperature_increment_on_fallback")
-    if temperature_increment_on_fallback is not None:
-        temperature = tuple(np.arange(temperature, 1.0 + 1e-6, temperature_increment_on_fallback))
+    if (increment := args.pop("temperature_increment_on_fallback")) is not None:
+        temperature = tuple(np.arange(temperature, 1.0 + 1e-6, increment))
     else:
         temperature = [temperature]
 
-    threads = args.pop("threads")
-    if threads > 0:
+    if (threads := args.pop("threads")) > 0:
         torch.set_num_threads(threads)
 
     from . import load_model
     model = load_model(model_name, device=device, download_root=model_dir)
 
     writer = get_writer(output_format, output_dir)
-
     for audio_path in args.pop("audio"):
         result = transcribe(model, audio_path, temperature=temperature, **args)
         writer(result, audio_path)